This invention relates to synthesizing software models of semiconductor process flows that are most likely able to fabricate desired semiconductor devices. More particularly, this invention relates to synthesizing such semiconductor process flow models using inverse modeling techniques.
The production of semiconductor devices involves a fabrication process. A fabrication process involves many variables, such as, for example, dopant concentrations and distributions in a semiconductor, environmental conditions (e.g., humidity, temperature, and cleanliness), physical parameters (e.g., film thicknesses, wafer sizes, device sizes, and numbers of layers), and electrical parameters (e.g., AC and DC characteristics, threshold voltages, transconductances, and capacitances). Needless to say, manually selecting particular combinations of variables or particular values for such variables when attempting to determine a particular process flow for fabricating a desired device or device characteristic is extremely complex with little assurance of success.
Thus, prior to fabricating a desired semiconductor device or desired characteristic, a potential process flow is typically modeled in software and then simulated. A process flow embodies each step of a fabrication or manufacturing process. Process flow modeling is often done because actual fabrication runs are expensive and time consuming, thus rendering a trial-and-error approach impractical. Modeling helps predict which potential process flows are most likely able to produce a device having desired characteristics. Thus, by modeling potential process flows, unnecessary and costly fabrication runs can be avoided.
Process flows are typically modeled using a physical model and a given set of modeling parameters. Physical models typically embody the physical, electrical, and other tangible properties of a particular device that has already been fabricated. This approach, however, has several problems. One problem is that the parameters provided to the process model may be inaccurate or insufficient. This can occur when a user does not have data that sufficiently characterizes the device to be fabricated. The process modeling tool may not then be able to accurately model a process flow.
Another problem is that the physical models themselves are often inaccurate. Models may be plagued with inaccuracies because technological advances typically progress faster than the actual understanding of the technology. This may make it difficult to construct models that accurately simulate process flows that can be used to produce desired devices.
The effects of these problems may be mitigated, if not rendered negligible, by using an inverse modeling technique. An inverse modeling technique uses “reverse engineering” to develop (or synthesize) a process that can fabricate a desired device. This technique has been used in the fields of geophysics, electromagnetism, and biotechnology. However, inverse modeling in the field of semiconductor fabrication has been at best limited.
In view of the foregoing, it would be desirable to synthesize semiconductor process flow models using an inverse modeling technique.